Preface
We are pleased to have you here as we document our research on AI Applications in rigorous business domains. This initiative proposes an engineering-oriented AI approach to fully automate management consulting services. All findings will be open-sourced, and we welcome enthusiasts to join us in shaping this transformative journey.
- Research Background: Limitations of AI in Business Content Creation
While large language models (LLMs) have matured in consumer-grade content generation and conversational interfaces, significant gaps remain when addressing commercial-grade AI demands. The core paradox lies in:
Robustness vs. Precise, Controllable and Trustworthy Outputs.
The technical paradox is: In terms of robustness requirement, the LLMs must maintain stable and reasonable outputs even with low-quality inputs. But for precision trade-off, when inputs are highly structured to meet specific output requirements, overgeneralization may degrade output accuracy.
This contradiction is magnified in business scenarios. Corporate data demands strict adherence to indicator definitions, multi-dimensional analysis, and granular traceability—requirements current AI systems struggle to fulfill. For instance, generating financial reports or operational analyses requires. Thus:
While AI excels in computational power,
but “simple” business calculations often determine success or failure
This limitation defines our core challenge in automating corporate consulting. Our solution?
AI-Driven Consulting Engineering
- Solution: Consulting Engineering Methodology
- Input-Output Workflow Reconstruction
Corporate consulting, as an experience-driven cognitive service, requires domain-specific knowledge processing. Let’s dissect the workflow using a mature listed company as a case study:
| Stakeholder | Objective | Knowledge Logic | Interaction Mode |
| Investor | Financial data insights | Universal financial frameworks | One-way output |
| Executive | Root-cause analysis of operational issues | Industry-specific causal models | Semi-dynamic modeling |
| Implementor | Actionable business decisions | Cross-domain decision logic | Iterative collaboration |
Complexity Gradient: Data input-output difficulty increases from investors to executors.
- Engineering Framework
We introduce AI-driven Consulting Engineering to achieve precision and trustworthiness across industries. The process is formalized as:
Y = F0 (F1 (d1 ) , F2 ( d2 ) , k1 , k2 )
| Component | Definition |
| Y | Visualized outputs (insights, root causes, recommendations) |
| F₀ (Visualization) | Markdown-based rendering engine for dynamic formatting (e.g., reports, dashboards) |
| F₁ (Trust Engineering) | Data governance with indicator definitions, traceability, and validation |
| d₁ (External Data) | Real-time external data (APIs, web scraping) |
| F₂ (Digital Twin) | Enterprise system digitization and encrypted data modeling |
| d₂ (Internal Data) | Structured process/transaction data from corporate systems |
| k₁ (Knowledge Graphs) | Industry-specific causal graphs linking outcomes to root causes |
| k₂ (Knowledge Graphs) | Domain-specific causal graphs linking outcomes to root causes |
Key Components:
- Visualization Engineering (F₀): from the simple way
- Uses Markdown + HTML templates for format-agnostic rendering.
- Example:
# Revenue Growth\n- Q1: +15% YoY\n- Driver: New Product Launch
→ PDF/PPT/HTML outputs.
- Trust Engineering (F₁):
- Ensures data integrity via metadata tagging (e.g., source, calculation logic).
- Example: Linking “30% cost reduction” to specific operational KPIs.
- Digital Twin Engineering (F₂):
- Constructs real-time digital replicas of corporate processes.
- Enables automated root-cause analysis (e.g., detecting supply chain bottlenecks via IoT data).
- Future Implications: Can AI Fully Replace Human Expertise
Current AI systems remain confined to static training datasets and real-time search augmentation—they lack the temporal continuity of human experience. Key questions:
- Where does AI’s training data originate? From humans who continuously evolve within the temporal flow.
- Can AI replicate dynamic, context-rich human ‘experience bodies’?
In the near term, AI-Human symbiosis will dominate consulting engineering:
- AI: Handles 80% of routine tasks (data processing, pattern recognition).
- Humans: Resolve 20% of edge cases requiring contextual judgment and creativity.
Call to Action:
This is a living document. We invite researchers, practitioners, and developers to contribute tools, datasets, and critiques. Together, we’ll redefine corporate intelligence.
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